In this paper, we present a method for pose-estimate-based target tracking (PBTT) that enables the performance of autonomous aerial manipulation operations in unstructured environments using fully on-board computation for both UAV localization and target tracking. The PBTT method does not depend on extracting traditional visual features (e.g. using SIFT, SURF, ORB, etc.) on or near the target. Instead, the algorithm combines input from an RGB-D camera and the UAV's position estimator (which utilizes a downward-facing optical flow camera for horizontal localization) to track a target point selected by a human operator. The effectiveness of the PBTT method is evaluated through several autonomous flight tests performed with the Interacting-Boomcopter (I-BC) UAV platform in unstructured environments and in the presence of light wind disturbances.

Current approaches to physical security suffer from high false alarm rates and frequent human operator involvement, despite the relative rarity of real-world threats. We present a novel architecture for autonomous adaptive physical security called autonomous detection and assessment with moving sensors (ADAMS). ADAMS is a framework for reducing nuisance and false alarms by placing mobile robotic platforms equipped with sensors outside the normal asset perimeter. These robotic agents integrate sensor data from multiple perspectives over time, and autonomously move to obtain the best new data to reduce uncertainty in the threat scene. Inferences drawn from data fused over time provide ultimate decisions regarding whether to alert human operators. This paper describes the framework and algorithms used in a prototype ADAMS implementation. We describe the results of simulations comparing this framework to alternate paradigms. These simulations show ADAMS has a 4x increase in the range at which threats are identified versus traditional static sensors, and a 5x reduction in false alarms triggered versus frameworks where all sensor detections become alarms, leading to reduced operator load. Further, these simulations show this framework for reacting to new potential threats significantly outperforms methods which merely patrol the site. We also present the results of preliminary hardware trials of an exemplar prototype system, providing limited validation of the simulations in a real-time physical demonstration.

This paper presents a novel approach for autonomously mounting a sensor package on a vertical surface with an unmanned aerial vehicle (UAV). The Interacting-BoomCopter (I-BC) UAV uses an on-board webcam and computer along with a horizontally-mounted reversible propeller on its front boom to autonomously perform the aerial manipulation task. An overview of the vehicle design is presented along with the image processing algorithms used for target tracking, and the implementation of an extended finite state machine (EFSM) for carrying out the high-level autonomous control. The effectiveness of the autonomous control strategy and I-BC platform are examined through the performance of several autonomous sensor mounting flight tests.

This paper presents the design of the Interacting-BoomCopter (I-BoomCopter) unmanned aerial vehicle (UAV), designed specifically for environmental interactions. The novel design consists of a horizontally mounted four-blade reversible propeller on a front boom that is attached to a standard tri-rotor UAV configuration. A custom end-effector and force sensor is placed at the end of the front boom for autonomous pushing and pulling interaction tasks. The modeling of the new platform is presented and two prototype versions built, flight tested, and characterized. Finally, the efficacy of the new platform for environmental interactions is evaluated with an autonomous door opening task and a teleoperated door opening and door closing task.